Last updated: 2024-01-23

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Knit directory: UPF1-FMR1/

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Introduction

library(eisaR)
source("code/libraries.R")
source("/home/neuro/Documents/NMD_analysis/other_repos/DiffRAC/DiffRAC.R")
source("code/codes.R")
library(coin)
library(gplots)
library(stargazer)
library(EnsDb.Hsapiens.v86)
library(org.Hs.eg.db)
txdf = transcripts(EnsDb.Hsapiens.v86, return.type="DataFrame")
tx2gene = as.data.frame(txdf[,c("tx_id","gene_id", "tx_biotype")])

RNA stability analysis - all data

sampleTable= read.csv("/home/neuro/Documents/NMD_analysis/Analysis/UPF1-FMR1/data/data-stability-no-FMR1.csv", header=TRUE, sep = ",")
inputFolder="/home/neuro/Documents/NMD_analysis/Analysis/Results/UPF1-FMR1/Stability/quantified/"
salmon.files = ("/home/neuro/Documents/NMD_analysis/Analysis/Results/UPF1-FMR1/Salmon")
salmon = list.files(salmon.files, pattern = "transcripts$", full.names = TRUE)
sample_names = gsub("/home/neuro/Documents/NMD_analysis/Analysis/Results/UPF1-FMR1/Salmon/", "", salmon)
sample_names = gsub(".gz_transcripts", "", sample_names)
sample_names = gsub("\\_.*", "", sample_names)
md = read.csv(here::here("data/Sample_info.csv"), header= TRUE) %>%  
  #mutate(files = file.path(salmon, "quant.sf")) %>% 
  dplyr::rename("names" = "GeneWiz.ID", 
                "Group" = "Sample.type") %>% 
  mutate(Group = ifelse(Group == "MC" | Group == "FC", "Control", Group)) %>%
  dplyr::select(names,everything()) %>% 
  mutate(names = gsub("\\_.*", "", names) )
md = md[order(match(md$names, sample_names)),]


md %<>% dplyr::filter(Group != "FMR1") %>%
 dplyr::filter(names != "23-LDJ6767") %>%
dplyr::filter(names != "202")
de_exonic  = DESeqDataSetFromHTSeqCount( # omits special rows from htseq-count
            sampleTable = sampleTable[sampleTable$ReadType=="exonic",],
            directory = inputFolder,
            design = ~ 1 # required parameter
        )

countsEx= counts(de_exonic)

de_intron = DESeqDataSetFromHTSeqCount( # omits special rows from htseq-count
            sampleTable = sampleTable[sampleTable$ReadType=="intronic",],
            directory = inputFolder,
            design = ~ 1 # required parameter
        )
countsIn = counts(de_intron)
md$Samples = colnames(countsEx)
rownames(md) = md$Samples
countsEx = countsEx[rownames(countsEx) %in% rownames(countsIn),]
countsIn = countsIn[rownames(countsIn) %in% rownames(countsEx),]
diffrac_res <- DiffRAC( ~Group + Sex+ Batch,
         md,
         countsEx,countsIn,
         "sample",
         optimizeBias = T)

Initializing DiffRAC framework...

Estimating size factors and dispersions...

Optimizing the bias constant...
0.381966011250105 : 1701754.53572386
0.618033988749895 : 1862941.554938
0.76393202250021 : 1906403.68805911
0.838792542308931 : 1916870.8253327
0.898997746487396 : 1920759.5604013
0.926877442866788 : 1921397.35589651
0.937090887858652 : 1921462.15023998
0.939292836886588 : 1921464.79709878
0.93964305261623 : 1921464.86724234
0.939976399951336 : 1921464.81307705
0.93964305261623 : 1921464.86724234
The bias constant is 0.93964305261623

Re-estimating dispersion...

Fitting model parameters...
res <- as.data.frame(results(diffrac_res$dds,name = "GroupUPF1.Ratio"))
res_sig = res %>% dplyr::filter(padj < 0.05)
res %<>%
  rownames_to_column("ensembl_gene_id") %>% 
    mutate(gene = mapIds(org.Hs.eg.db, keys=ensembl_gene_id,  column="SYMBOL",keytype="ENSEMBL", multiVals="first"), 
           entrez = mapIds(org.Hs.eg.db, keys=ensembl_gene_id,  column="ENTREZID",keytype="ENSEMBL", multiVals="first")) %>% drop_na(entrez) %>% 
  mutate(Expression= ifelse(log2FoldChange > 0 & padj < 0.05, "Upregulated", 
                            ifelse(log2FoldChange < 0 & padj < 0.05, "Downregulated", "Not Sig")))
DEColours <- c("Downregulated" = "#2e294e","Upregulated" = "#720026",  "NotSig" = "#E5E5E5")


volc_upf1 = res %>%
  mutate(Expression= ifelse(log2FoldChange > 0 & padj < 0.05, "Upregulated", 
                            ifelse(log2FoldChange < 0 & padj < 0.05, "Downregulated", "Not Sig"))) %>%
    ggplot(aes(y = -log10(padj), 
               x =  log2FoldChange, 
               colour = Expression,
               size =-log10(padj), 
               label= gene)) +
  geom_point(alpha = 0.8) +
  # geom_text(aes(label=ifelse(SYMBOL== "Upf1",as.character(SYMBOL),''))) +
  #  geom_text(aes(label= SYMBOL), subset = SYMBOL == "Upf1") +
    scale_colour_manual(values = DEColours) + theme_classic() + 
    theme(axis.title.y = element_text(size = 12)) +
    geom_hline(yintercept = -log10(0.05), color = "grey60", size = 0.5, lty = "dashed") +
    labs(x = "log2 Fold Change", y = "-log10 adj p-value") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") +
  xlim(-8.5, 8.5) + ylim(0, 7.5)


# volc = volc_upf1+ geom_text_repel(data=subset(upf1_results_lfc , SYMBOL %in% c("UPF1", "UPF2", "UPF3B",
#                       "SMG5", "SMG6",
#                       "UPF3A", "ATF4", 
#                       "GADD5G")),
#             aes(label=SYMBOL),   position=position_dodge(width = 0.9), 
#              vjust=-0.40, color = "black", box.padding = 0.5, fill = "white") + ggtitle("DEGs in UPF1 relative to controls using limma/voom")
# 
# 
# volc 

my_gg = volc_upf1 + geom_point_interactive(aes(tooltip =gene, data_id = gene), 
    size = 1, hover_nearest = TRUE)
girafe(ggobj = my_gg)

Volcano plot showing distribution of differentially stabilized / destabilized genes

  • FMR1
res <- as.data.frame(results(diffrac_res$dds,name = "GroupFRAX.Ratio"))
res_sig = res %>% dplyr::filter(padj < 0.05)
res %<>%
  rownames_to_column("ensembl_gene_id") %>% 
    mutate(gene = mapIds(org.Hs.eg.db, keys=ensembl_gene_id,  column="SYMBOL",keytype="ENSEMBL", multiVals="first"), 
           entrez = mapIds(org.Hs.eg.db, keys=ensembl_gene_id,  column="ENTREZID",keytype="ENSEMBL", multiVals="first")) %>% drop_na(entrez) %>% 
  mutate(Expression= ifelse(log2FoldChange > 0 & padj < 0.05, "Upregulated", 
                            ifelse(log2FoldChange < 0 & padj < 0.05, "Downregulated", "Not Sig")))
DEColours <- c("Downregulated" = "#2e294e","Upregulated" = "#720026",  "NotSig" = "#E5E5E5")


volc_fmr1 = res %>%
  mutate(Expression= ifelse(log2FoldChange > 0 & padj < 0.05, "Upregulated", 
                            ifelse(log2FoldChange < 0 & padj < 0.05, "Downregulated", "Not Sig"))) %>%
    ggplot(aes(y = -log10(padj), 
               x =  log2FoldChange, 
               colour = Expression,
               size =-log10(padj), 
               label= gene)) +
  geom_point(alpha = 0.8) +
  # geom_text(aes(label=ifelse(SYMBOL== "Upf1",as.character(SYMBOL),''))) +
  #  geom_text(aes(label= SYMBOL), subset = SYMBOL == "Upf1") +
    scale_colour_manual(values = DEColours) + theme_classic() + 
    theme(axis.title.y = element_text(size = 12)) +
    geom_hline(yintercept = -log10(0.05), color = "grey60", size = 0.5, lty = "dashed") +
    labs(x = "log2 Fold Change", y = "-log10 adj p-value") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") +
  xlim(-8.5, 8.5) + ylim(0, 7.5)


# volc = volc_upf1+ geom_text_repel(data=subset(upf1_results_lfc , SYMBOL %in% c("UPF1", "UPF2", "UPF3B",
#                       "SMG5", "SMG6",
#                       "UPF3A", "ATF4", 
#                       "GADD5G")),
#             aes(label=SYMBOL),   position=position_dodge(width = 0.9), 
#              vjust=-0.40, color = "black", box.padding = 0.5, fill = "white") + ggtitle("DEGs in UPF1 relative to controls using limma/voom")
# 
# 
# volc 

my_gg = volc_fmr1 + geom_point_interactive(aes(tooltip =gene, data_id = gene), 
    size = 1, hover_nearest = TRUE)
girafe(ggobj = my_gg)

Volcano plot showing distribution of differentially stabilized / destabilized genes


sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
 [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
 [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

time zone: Australia/Adelaide
tzcode source: system (glibc)

attached base packages:
 [1] grid      stats4    tools     stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] plyr_1.8.9                  org.Hs.eg.db_3.18.0        
 [3] EnsDb.Hsapiens.v86_2.99.0   stargazer_5.2.3            
 [5] gplots_3.1.3                coin_1.4-3                 
 [7] survival_3.5-7              ngsReports_2.4.0           
 [9] patchwork_1.2.0             AnnotationHub_3.10.0       
[11] BiocFileCache_2.10.1        dbplyr_2.4.0               
[13] openxlsx_4.2.5.2            ggiraph_0.8.8              
[15] DT_0.31                     msigdb_1.10.0              
[17] GSEABase_1.64.0             graph_1.80.0               
[19] annotate_1.80.0             XML_3.99-0.16              
[21] pheatmap_1.0.12             ggvenn_0.1.10              
[23] MetBrewer_0.2.0             ggpubr_0.6.0               
[25] venn_1.12                   viridis_0.6.4              
[27] viridisLite_0.4.2           tximeta_1.20.2             
[29] tximport_1.30.0             goseq_1.54.0               
[31] geneLenDataBase_1.38.0      BiasedUrn_2.0.11           
[33] org.Mm.eg.db_3.18.0         EnsDb.Mmusculus.v79_2.99.0 
[35] ensembldb_2.26.0            AnnotationFilter_1.26.0    
[37] GenomicFeatures_1.54.1      AnnotationDbi_1.64.1       
[39] biomaRt_2.58.0              edgeR_4.0.11               
[41] limma_3.58.1                DESeq2_1.42.0              
[43] SummarizedExperiment_1.32.0 Biobase_2.62.0             
[45] MatrixGenerics_1.14.0       matrixStats_1.2.0          
[47] GenomicRanges_1.54.1        GenomeInfoDb_1.38.5        
[49] IRanges_2.36.0              S4Vectors_0.40.2           
[51] BiocGenerics_0.48.1         corrplot_0.92              
[53] lubridate_1.9.3             forcats_1.0.0              
[55] purrr_1.0.2                 readr_2.1.5                
[57] tidyverse_2.0.0             stringr_1.5.1              
[59] tidyr_1.3.0                 scales_1.3.0               
[61] data.table_1.14.10          readxl_1.4.3               
[63] tibble_3.2.1                magrittr_2.0.3             
[65] reshape2_1.4.4              ggplot2_3.4.4              
[67] dplyr_1.1.4                 eisaR_1.14.1               
[69] workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] fs_1.6.3                      ProtGenerics_1.34.0          
  [3] bitops_1.0-7                  httr_1.4.7                   
  [5] RColorBrewer_1.1-3            backports_1.4.1              
  [7] utf8_1.2.4                    R6_2.5.1                     
  [9] lazyeval_0.2.2                mgcv_1.9-1                   
 [11] withr_3.0.0                   prettyunits_1.2.0            
 [13] gridExtra_2.3                 cli_3.6.2                    
 [15] sandwich_3.1-0                labeling_0.4.3               
 [17] sass_0.4.8                    mvtnorm_1.2-4                
 [19] Rsamtools_2.18.0              systemfonts_1.0.5            
 [21] rstudioapi_0.15.0             RSQLite_2.3.5                
 [23] generics_0.1.3                BiocIO_1.12.0                
 [25] gtools_3.9.5                  car_3.1-2                    
 [27] zip_2.3.0                     GO.db_3.18.0                 
 [29] Matrix_1.6-5                  fansi_1.0.6                  
 [31] abind_1.4-5                   lifecycle_1.0.4              
 [33] whisker_0.4.1                 multcomp_1.4-25              
 [35] yaml_2.3.8                    carData_3.0-5                
 [37] SparseArray_1.2.3             blob_1.2.4                   
 [39] promises_1.2.1                crayon_1.5.2                 
 [41] lattice_0.22-5                KEGGREST_1.42.0              
 [43] pillar_1.9.0                  knitr_1.45                   
 [45] rjson_0.2.21                  admisc_0.34                  
 [47] codetools_0.2-19              glue_1.7.0                   
 [49] getPass_0.2-4                 vctrs_0.6.5                  
 [51] png_0.1-8                     cellranger_1.1.0             
 [53] gtable_0.3.4                  cachem_1.0.8                 
 [55] xfun_0.41                     S4Arrays_1.2.0               
 [57] mime_0.12                     libcoin_1.0-10               
 [59] statmod_1.5.0                 interactiveDisplayBase_1.40.0
 [61] ellipsis_0.3.2                TH.data_1.1-2                
 [63] nlme_3.1-164                  bit64_4.0.5                  
 [65] progress_1.2.3                filelock_1.0.3               
 [67] rprojroot_2.0.4               bslib_0.6.1                  
 [69] KernSmooth_2.23-22            colorspace_2.1-0             
 [71] DBI_1.2.1                     tidyselect_1.2.0             
 [73] processx_3.8.3                bit_4.0.5                    
 [75] compiler_4.3.2                curl_5.2.0                   
 [77] git2r_0.33.0                  xml2_1.3.6                   
 [79] ggdendro_0.1.23               DelayedArray_0.28.0          
 [81] plotly_4.10.4                 rtracklayer_1.62.0           
 [83] caTools_1.18.2                callr_3.7.3                  
 [85] rappdirs_0.3.3                digest_0.6.34                
 [87] rmarkdown_2.25                XVector_0.42.0               
 [89] htmltools_0.5.7               pkgconfig_2.0.3              
 [91] highr_0.10                    fastmap_1.1.1                
 [93] rlang_1.1.3                   htmlwidgets_1.6.4            
 [95] shiny_1.8.0                   farver_2.1.1                 
 [97] jquerylib_0.1.4               zoo_1.8-12                   
 [99] jsonlite_1.8.8                BiocParallel_1.36.0          
[101] RCurl_1.98-1.14               modeltools_0.2-23            
[103] GenomeInfoDbData_1.2.11       munsell_0.5.0                
[105] Rcpp_1.0.12                   stringi_1.8.3                
[107] zlibbioc_1.48.0               MASS_7.3-60.0.1              
[109] parallel_4.3.2                Biostrings_2.70.1            
[111] splines_4.3.2                 pander_0.6.5                 
[113] hms_1.1.3                     locfit_1.5-9.8               
[115] ps_1.7.6                      uuid_1.2-0                   
[117] ggsignif_0.6.4                BiocVersion_3.18.1           
[119] evaluate_0.23                 BiocManager_1.30.22          
[121] tzdb_0.4.0                    httpuv_1.6.13                
[123] broom_1.0.5                   xtable_1.8-4                 
[125] restfulr_0.0.15               rstatix_0.7.2                
[127] later_1.3.2                   memoise_2.0.1                
[129] GenomicAlignments_1.38.2      timechange_0.3.0             
[131] here_1.0.1